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Marlos C. Machado is an Assistant Professor at the Department of Computing Science at the University of Alberta, a Canada CIFAR AI Chair, and a Fellow at the Alberta Machine Intelligence Institute (Amii). Before becoming a professor, he was a researcher at DeepMind and Google Brain for four years. He received his B.Sc. and M.Sc. from UFMG, Brazil, and his Ph.D. from the University of Alberta, Canada, all in Computing Science. His research interests lie broadly in machine learning, specifically in (deep) reinforcement learning, representation learning, continual learning, and real-world applications of all of the above. His research has been featured in popular media such as BBC, Bloomberg TV, The Verge, and Wired. He has received several recognitions, including the AAAI’25 New Faculty Highlight and best paper awards runner-up at AAMAS’16 and AISTATS’22. Currently, he serves as an action editor for the Transactions on Machine Learning Research journal, as an editor for the Reinforcement Learning Journal, and is the program co-chair of the 2nd Reinforcement Learning Conference (RLC 2025).
Talk: "Representation-driven Option Discovery in Reinforcement Learning"
Natasha Jaques is an Assistant Professor of Computer Science and Engineering at the University of Washington, and a Senior Research Scientist at Google DeepMind. Her research focuses on Social Reinforcement Learning in multi-agent and human-AI interactions. During her PhD at MIT, she developed foundational techniques for training language models with Reinforcement Learning from Human Feedback (RLHF). In the multi-agent space, she has developed techniques for improving coordination through social influence, and unsupervised environment design. Natasha’s work has received various awards, including Best Demo at NeurIPS, an honourable mention for Best Paper at ICML, and the Outstanding PhD Dissertation Award from the Association for the Advancement of Affective Computing. Her work has been featured in Science Magazine, MIT Technology Review, Quartz, IEEE Spectrum, Boston Magazine, and on CBC radio, among others. Natasha earned her Masters degree from the University of British Columbia, undergraduate degrees in Computer Science and Psychology from the University of Regina, and was a postdoctoral fellow at UC Berkeley.
Talk: "Social Reinforcement Learning"
Roberta Raileanu is a Senior Staff Research Scientist at Google DeepMind in the Open-Endedness team. She is also an Honorary Lecturer at UCL, advising PhD students and co-teaching a course on Open-Endedness and General Intelligence. Previously, she led the AI Engineer and AI Scientist team at Meta GenAI in London, focused on building AI agents that drive scientific discovery through planning, reasoning, tool use, and learning from feedback. She also led the Tool Use team for Llama 3 and contributed to products like Meta AI and AI Studio. Her research interests include reinforcement learning, open-ended learning, self-supervised learning, and large language models—particularly in building robust, generalizable, and aligned AI agents that can learn continuously and act autonomously in complex environments.
Talk: "LLM Whispers: Injecting Human Priors into RL Agents"
Pablo Samuel Castro was born and raised in Quito, Ecuador, and moved to Montréal after high school to study at McGill University. For his PhD, he studied reinforcement learning with Doina Precup and Prakash Panangaden at McGill. Castro has been working at Google for over eleven years. He is currently a staff research scientist at Google DeepMind in Montreal, where he conducts fundamental reinforcement learning research and is a regular advocate for increasing LatinX representation in the research community. He is also an adjunct professor in the Department of Computer Science and Operations Research (DIRO) at Université de Montréal. In addition to his interest in coding, AI and math, Castro is an active musician.
Talk: "Network Plasticity and Scalability in Deep Reinforcement Learning"
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